Interference suppression techniques

ABSTRACT

System ( 10 ) is disclosed including an acoustic sensor array ( 20 ) coupled to processor ( 42 ). System ( 10 ) processes inputs from array ( 20 ) to extract a desired acoustic signal through the suppression of interfering signals. The extraction/suppression is performed by modifying the array ( 20 ) inputs in the frequency domain with weights selected to minimize variance of the resulting output signal while maintaining unity gain of signals received in the direction of the desired acoustic signal. System ( 10 ) may be utilized in hearing aids, voice input devices, surveillance devices, and other applications.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. patentapplication Ser. No. No. 09/568,430 filed on May 10, 2000, and isrelated to: U.S. patent application Ser. No. No. 09/193,058 filed on 16Nov. 1998, which is a continuation-in-part of U.S. patent applicationSer. No. 08/666,757 filed Jun. 19, 1996 (now U.S. Pat. No. 6,222,927B1);

U.S. patent application Ser. No. 09/568,435 filed on May 10, 2000; andU.S. patent application Ser. No. 09/805,233 filed on Mar. 13, 2001,which is a continuation of International Patent Application NumberPCT/US99/26965, all of which are hereby incorporated by reference.

GOVERNMENT RIGHTS

The U.S. Government has a paid-up license in this invention and theright in limited circumstances to require the patent owner to licenseothers on reasonable terms as provided for by DARPA Contract No. ARMYSUNY240-6762A and National Institutes of Health Contract No. R21DC04840.

BACKGROUND OF THE INVENTION

The present invention is directed to the processing of acoustic signals,and more particularly, but not exclusively, relates to techniques toextract an acoustic signal from a selected source while suppressinginterference from other sources using two or more microphones.

The difficulty of extracting a desired signal in the presence ofinterfering signals is a long-standing problem confronted by acousticengineers. This problem impacts the design and construction of manykinds of devices such as systems for voice recognition and intelligencegathering. Especially troublesome is the separation of desired soundfrom unwanted sound with hearing, aid devices. Generally, hearing aiddevices do not permit selective amplification of a desired sound whencontaminated by noise from a nearby source. This problem is even moresevere when the desired sound is a speech signal and the nearby noise isalso a speech signal produced by other talkers. As used herein, “noise”refers not only to random or nondeterministic signals, but also toundesired signals and signals interfering with the perception of adesired signal.

SUMMARY OF THE INVENTION

One form of the present invention includes a unique signal processingtechnique using two or more microphones. Other forms include uniquedevices and methods for processing acoustic signals.

Further embodiments, objects, features, aspects, benefits, forms, andadvantages of the present invention shall become apparent from thedetailed drawings and descriptions provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic view of a signal processing system.

FIG. 2 is a diagram further depicting selected aspects of the system ofFIG. 1.

FIG. 3 is a flow chart of a routine for operating the system of FIG. 1.

FIGS. 4 and 5 depict other embodiments of the present inventioncorresponding to hearing aid and computer voice recognition applicationsof the system of FIG. 1, respectively.

FIG. 6 is a diagrammatic view of an experimental setup of the system ofFIG. 1.

FIG. 7 is a graph of magnitude versus time of a target speech signal andtwo interfering speech signals.

FIG. 8 is a graph of magnitude versus time of a composite of the speechsignals of FIG. 7 before processing, an extracted signal correspondingto the target speech signal of FIG. 7, and a duplicate of the targetspeech signal of FIG. 7 for comparison.

FIG. 9 is a graph providing line plots for regularization factor (M)values of 1.001, 1.005, 1.01, and 1.03 in terms of beamwidth versusfrequency.

FIG. 10 is a flowchart of a procedure that can be performed with thesystem of FIG. 1 either with or without the routine of FIG. 3.

FIGS. 11 and 12 are graphs illustrating the efficacy of the procedure ofFIG. 10.

DESCRIPTION OF SELECTED EMBODIMENTS

While the present invention can take many different forms, for thepurpose of promoting an understanding of the principles of theinvention, reference will now be made to the embodiments illustrated inthe drawings and specific language will be used to describe the same. Itwill nevertheless be understood that no limitation of the scope of theinvention is thereby intended. Any alterations and further modificationsof the described embodiments, and any further applications of theprinciples of the invention as described herein are contemplated aswould normally occur to one skilled in the art to which the inventionrelates.

FIG. 1 illustrates an acoustic signal processing system 10 of oneembodiment of the present invention. System 10 is configured to extracta desired acoustic excitation from acoustic source 12 in the presence ofinterference or noise from other sources, such as acoustic sources 14,16. System 10 includes acoustic sensor array 20. For the exampleillustrated, sensor array 20 includes a pair of acoustic sensors 22, 24within the reception range of sources 12, 14, 16. Acoustic sensors 22,24 are arranged to detect acoustic excitation from sources 12, 14, 16.

Sensors 22, 24 are separated by distance D as illustrated by the likelabeled line segment along lateral axis T. Lateral axis T isperpendicular to azimuthal axis AZ. Midpoint M represents the halfwaypoint along distance D from sensor 22 to sensor 24. Axis AZ intersectsmidpoint M and acoustic source 12. Axis AZ is designated as a point ofreference (zero degrees) for sources 12, 14, 16 in the azimuthal planeand for sensors 22, 24. For the depicted embodiment, sources 14, 16define azimuthal angles 14 a, 16 a relative to axis AZ of about +22° and−65°, respectively. Correspondingly, acoustic source 12 is at 0°relative to axis AZ. In one mode of operation of system 10, the “onaxis” alignment of acoustic source 12 with axis AZ selects it as adesired or target source of acoustic excitation to be monitored withsystem 10. In contrast, the “off-axis” sources 14, 16 are treated asnoise and suppressed by system 10, which is explained in more detailhereinafter. To adjust the direction being monitored, sensors 22, 24 canbe moved to change the position of axis AZ. In an additional oralternative operating mode, the designated monitoring direction can beadjusted by changing a direction indicator incorporated in the routineof FIG. 3 as more fully described below. For these operating modes, itshould be understood that neither sensor 22 nor 24 needs to be moved tochange the designated monitoring direction, and the designatedmonitoring direction need not be coincident with axis AZ.

In one embodiment, sensors 22, 24 are omnidirectional dynamicmicrophones. In other embodiments, a different type of microphone, suchas cardioid or hypercardioid variety could be utilized, or suchdifferent sensor type can be utilized as would occur to one skilled inthe art. Also, in alternative embodiments more or fewer acoustic sourcesat different azimuths may be present; where the illustrated number andarrangement of sources 12, 14, 16 is provided as merely one of manyexamples. In one such example, a room with several groups of individualsengaged in simultaneous conversation may provide a number of thesources.

Sensors 22, 24 are operatively coupled to processing subsystem 30 toprocess signals received therefrom. For the convenience of description,sensors 22, 24 are designated as belonging to left channel L and rightchannel R, respectively. Further, the analog time domain signalsprovided by sensors 22, 24 to processing subsystem 30 are designatedx_(L)(t) and x_(R)(t) for the respective channels L and R. Processingsubsystem 30 is operable to provide an output signal that suppressesinterference from sources 14, 16 in favor of acoustic excitationdetected from the selected acoustic source 12 positioned along axis AZ.This output signal is provided to output device 90 for presentation to auser in the form of an audible or visual signal which can be furtherprocessed.

Referring additionally to FIG. 2, a diagram is provided that depictsother details of system 10. Processing subsystem 30 includes signalconditioner/filters 32 a and 32 b to filter and condition input signalsx_(L)(t) and x_(R)(t) from sensors 22, 24; where t represents time.After signal conditioner/filter 32 a and 32 b, the conditioned signalsare input to corresponding Analog-to-Digital (A/D) converters 34 a, 34 bto provide discrete signals x_(L)(Z) and x_(R)(z), for channels L and R,respectively; where z indexes discrete sampling events. The samplingrate f_(S) is selected to provide desired fidelity for a frequency rangeof interest. Processing subsystem 30 also includes digital circuitry 40comprising processor 42 and memory 50. Discrete signals x_(L)(z) andx_(R)(z) are stored in sample buffer 52 of memory 50 in aFirst-In-First-Out (FIFO) fashion.

Processor 42 can be a software or firmware programmable device, a statelogic machine, or a combination of both programmable and dedicatedhardware. Furthermore, processor 42 can be comprised of one or morecomponents and can include one or more Central Processing Units (CPUs).In one embodiment, processor 42 is in the form of a digitallyprogrammable, highly integrated semiconductor chip particularly suitedfor signal processing. In other embodiments, processor 42 may be of ageneral purpose type or other arrangement as would occur to thoseskilled in the art.

Likewise, memory 50 can be variously configured as would occur to thoseskilled in the art. Memory 50 can include one or more types ofsolid-state electronic memory, magnetic memory, or optical memory of thevolatile and/or nonvolatile variety. Furthermore, memory can be integralwith one or more other components of processing subsystem 30 and/orcomprised of one or more distinct components.

Processing subsystem 30 can include any oscillators, control clocks,interfaces, signal conditioners, additional filters, limiters,converters, power supplies, communication ports, or other types ofcomponents as would occur to those skilled in the art to implement thepresent invention. In one embodiment, subsystem 30 is provided in theform of a single microelectronic device.

Referring also to the flow chart of FIG. 3, routine 140 is illustrated.Digital circuitry 40 is configured to perform routine 140. Processor 42executes logic to perform at least some the operations of routine 140.By way of nonlimiting example, this logic can be in the form of softwareprogramming instructions, hardware, firmware, or a combination of these.The logic can be partially or completely stored on memory 50 and/orprovided with one or more other components or devices. By way ofnonlimiting example, such logic can be provided to processing subsystem30 in the form of signals that are carried by a transmission medium suchas a computer network or other wired and/or wireless communicationnetwork.

In stage 142, routine 140 begins with initiation of the A/D sampling andstorage of the resulting discrete input samples x_(L)(z) and x_(R)(z) inbuffer 52 as previously described. Sampling is performed in parallelwith other stages of routine 140 as will become apparent from thefollowing description. Routine 140 proceeds from stage 142 toconditional 144. Conditional 144 tests whether routine 140 is tocontinue. If not, routine 140 halts. Otherwise, routine 140 continueswith stage 146. Conditional 144 can correspond to an operator switch,control signal, or power control associated with system 10 (not shown).

In stage 146, a fast discrete fourier transform (FFT) algorithm isexecuted on a sequence of samples x_(L)(Z) and x_(R)(z) and stored inbuffer 54 for each channel L and R to provide corresponding frequencydomain signals X_(L)(k) and X_(R)(k); where k is an index to thediscrete frequencies of the FFTs (alternatively referred to as“frequency bins” herein). The set of samples x_(L)(z) and x_(R)(z) uponwhich an FFT is performed can be described in terms of a time durationof the sample data. Typically, for a given sampling rate f_(S), each FFTis based on more than 100 samples. Furthermore, for stage 146, FFTcalculations include application of a windowing technique to the sampledata. One embodiment utilizes a Hamming window. In other embodiments,data windowing can be absent or a different type utilized, the FFT canbe based on a different sampling approach, and/or a different transformcan be employed as would occur to those skilled in the art. After thetransformation, the resulting spectra X_(L)(k) and X_(R)(k) are storedin FFT buffer 54 of memory 50. These spectra are generallycomplex-valued.

It has been found that reception of acoustic excitation emanating from adesired direction can be improved by weighting and summing the inputsignals in a manner arranged to minimize the variance (or equivalently,the energy) of the resulting output signal while under the constraintthat signals from the desired direction are output with a predeterminedgain. The following relationship (1) expresses this linear combinationof the frequency domain input signals:Y(k)=W _(L)*(k)X _(L)(k)+W _(R)*(k)X _(R)(k)=W ^(H)(k)X(k);  (1)where: $\begin{matrix}{{{{W(k)} = \begin{bmatrix}{W_{L}(k)} \\{W_{R}(k)}\end{bmatrix}};}{{{X(k)} = \begin{bmatrix}{X_{L}(k)} \\{X_{R}(k)}\end{bmatrix}};}} & (1)\end{matrix}$Y(k) is the output signal in frequency domain form, W_(L)(k) andW_(R)(k) are complex valued multipliers (weights) for each frequency kcorresponding to channels L and R, the superscript “*” denotes thecomplex conjugate operation, and the superscript “H” denotes taking theHermitian of a vector. For this approach, it is desired to determine an“optimal” set of weights W_(L)(k) and W_(R)(k) to minimize variance ofY(k). Minimizing the variance generally causes cancellation of sourcesnot aligned with the desired direction. For the mode of operation wherethe desired direction is along axis AZ, frequency components which donot originate from directly ahead of the array are attenuated becausethey are not consistent in phase across the left and right channels L,R, and therefore have a larger variance than a source directly ahead.Minimizing the variance in this case is equivalent to minimizing theoutput power of off-axis sources, as related by the optimization goal ofrelationship (2) that follows: $\begin{matrix}{\underset{W}{Min}E\{ {{Y(k)}}^{2} \}} & (2)\end{matrix}$where Y(k) is the output signal described in connection withrelationship (1). In one form, the constraint requires that “on axis”acoustic signals from sources along the axis AZ be passed with unitygain as provided in relationship (3) that follows:e ^(H) W(k)=1  (3)Here e is a two element vector which corresponds to the desireddirection. When this direction is coincident with axis AZ, sensors 22and 24 generally receive the signal at the same time and amplitude, andthus, for source 12 of the illustrated embodiment, the vector e isreal-valued with equal weighted elements—for instance e^(H)=[0.5 0.5].In contrast, if the selected acoustic source is not on axis AZ, thensensors 22, 24 can be moved to align axis AZ with it.

In an additional or alternative mode of operation, the elements ofvector e can be selected to monitor along a desired direction that isnot coincident with axis AZ. For such operating modes, vector e becomescomplex-valued to represent the appropriate time/phase delays betweensensors 22, 24 that correspond to acoustic excitation off axis AZ. Thus,vector e operates as the direction indicator previously described.Correspondingly, alternative embodiments can be arranged to select adesired acoustic excitation source by establishing a different geometricrelationship relative to axis AZ. For instance, the direction formonitoring a desired source can be disposed at a nonzero azimuthal anglerelative to axis AZ. Indeed, by changing vector e, the monitoringdirection can be steered from one direction to another without movingeither sensor 22, 24. Procedure 520 described in connection with theflowchart of FIG. 10 hereinafter provides an example of alocalization/tracking routine that can be used in conjunction withroutine 140 to steer vector e.

For inputs X_(L)(k) and X_(R)(k) that generally correspond to stationaryrandom processes (which is typical of speech signals over small periodsof time), the following weight vector W(k) relationship (4) can bedetermined from relationships (2) and (3): $\begin{matrix}{{W(k)} = \frac{{R(k)}^{- 1}e}{e^{H}{R(k)}^{- 1}e}} & (4)\end{matrix}$where e is the vector associated with the desired reception direction,R(k) is the correlation matrix for the k^(th) frequency, W(k) is theoptimal weight vector for the k^(th) frequency and the superscript “−1”denotes the matrix inverse. The derivation of this relationship isexplained in connection with a general model of the present inventionapplicable to embodiments with more than two sensors 22, 24 in array 20.

The correlation matrix R(k) can be estimated from spectral data obtainedvia a number “F” of fast discrete Fourier transforms (FFTs) calculatedover a relevant time interval. For the two channel L, R embodiment, thecorrelation matrix for the k^(th) frequency, R(k), is expressed by thefollowing relationship (5): $\begin{matrix}\begin{matrix}{{R(k)} = \begin{bmatrix}{\frac{M}{F}{\sum\limits_{n = 1}^{F}{{X_{l}^{*}( {n,k} )}{X_{l}( {n,k} )}}}} & {\frac{1}{F}{\sum\limits_{n = 1}^{F}{{X_{l}^{*}( {n,k} )}{X_{r}( {n,k} )}}}} \\{\frac{1}{F}{\sum\limits_{n = 1}^{F}{{X_{r}^{*}( {n,k} )}{X_{l}( {n,k} )}}}} & {\frac{M}{F}{\sum\limits_{n = 1}^{F}{{X_{r}^{*}( {n,k} )}{X_{r}( {n,k} )}}}}\end{bmatrix}} \\{= \begin{bmatrix}{X_{ll}(k)} & {X_{lr}(k)} \\{X_{rl}(k)} & {X_{rr}(k)}\end{bmatrix}}\end{matrix} & (5)\end{matrix}$where X_(l) is the FFT in the frequency buffer for the left channel Land X_(r) is the FFT in the frequency buffer for right channel Robtained from previously stored FFTs that were calculated from anearlier execution of stage 146; “n” is an index to the number “F” ofFFTs used for the calculation; and “M” is a regularization parameter.The terms X_(ll)(k), X_(lr)(k), X_(rl)(k), and X_(rr)(k) represent theweighted sums for purposes of compact expression. It should beappreciated that the elements of the R(k) matrix are nonlinear, andtherefore Y(k) is a nonlinear function of the inputs.

Accordingly, in stage 148 spectra X_(l)(k) and X_(r)(k) previouslystored in buffer 54 are read from memory 50 in a First-In-First-Out(FIFO) sequence. Routine 140 then proceeds to stage 150. In stage 150,multiplier weights W_(L)(k), W_(R)(k) are applied to X_(l)(k) andX_(r)(k), respectively, in accordance with the relationship (1) for eachfrequency k to provide the output spectra Y(k). Routine 140 continueswith stage 152 which performs an Inverse Fast Fourier Transform (IFFT)to change the Y(k) FFT determined in stage 150 into a discrete timedomain form designated y(z). Next, in stage 154, a Digital-to-Analog(D/A) conversion is performed with D/A converter 84 (FIG. 2) to providean analog output signal y(t). It should be understood thatcorrespondence between Y(k) FFTs and output sample y(z) can vary. In oneembodiment, there is one Y(k) FFT output for every y(z), providing aone-to-one correspondence. In another embodiment, there may be one Y(k)FFT for every 16 output samples y(z) desired, in which case the extrasamples can be obtained from available Y(k) FFTs. In still otherembodiments, a different correspondence may be established.

After conversion to the continuous time domain form, signal y(t) isinput to signal conditioner/filter 86. Conditioner/filter 86 providesthe conditioned signal to output device 90. As illustrated in FIG. 2,output device 90 includes an amplifier 92 and audio output device 94.Device 94 may be a loudspeaker, hearing aid receiver output, or otherdevice as would occur to those skilled in the art. It should beappreciated that system 10 processes a binaural input to produce anmonaural output. In some embodiments, this output could be furtherprocessed to provide multiple outputs. In one hearing aid applicationexample, two outputs are provided that deliver generally the same soundto each ear of a user. In another hearing aid application, the soundprovided to each ear selectively differs in terms of intensity and/ortiming to account for differences in the orientation of the sound sourceto each sensor 22, 24, improving sound perception.

After stage 154, routine 140 continues with conditional 156. In manyapplications it may not be desirable to recalculate the elements ofweight vector W(k) for every Y(k). Accordingly, conditional 156 testswhether a desired time interval has passed since the last calculation ofvector W(k). If this time period has not lapsed, then control flows tostage 158 to shift buffers 52, 54 to process the next group of signals.From stage 158, processing loop 160 closes, returning to conditional144. Provided conditional 144 remains true, stage 146 is repeated forthe next group of samples of x_(L)(z) and x_(R)(Z) to determine the nextpair of X_(L)(k) and X_(R)(k) FFTs for storage in buffer 54. Also, witheach execution of processing loop 160, stages 148, 150, 152, 154 arerepeated to process previously stored X_(l)(k) and X_(r)(k) FFTs todetermine the next Y(k) FFT and correspondingly generate a continuousy(t). In this manner buffers 52, 54 are periodically shifted in stage158 with each repetition of loop 160 until either routine 140 halts astested by conditional 144 or the time period of conditional 156 haslapsed.

If the test of conditional 156 is true, then routine 140 proceeds fromthe affirmative branch of conditional 156 to calculate the correlationmatrix R(k) in accordance with relationship (5) in stage 162. From thisnew correlation matrix R(k), an updated vector W(k) is determined inaccordance with relationship (4) in stage 164. From stage 164, updateloop 170 continues with stage 158 previously described, and processingloop 160 is re-entered until routine 140 halts per conditional 144 orthe time for another recalculation of vector W(k) arrives. Notably, thetime period tested in conditional 156 may be measured in terms of thenumber of times loop 160 is repeated, the number of FFTs or samplesgenerated between updates, and the like. Alternatively, the periodbetween updates can be dynamically adjusted based on feedback from anoperator or monitoring device (not shown).

When routine 140 initially starts, earlier stored data is not generallyavailable. Accordingly, appropriate seed values may be stored in buffers52, 54 in support of initial processing. In other embodiments, a greaternumber of acoustic sensors can be included in array 20 and routine 140can be adjusted accordingly. For this more general form, the output canbe expressed by relationship (6) as follows:Y(k)=W ^(H)(k)X(k)  (6)where the X(k) is a vector with an entry for each of “C” number of inputchannels and the weight vector W(k) is of like dimension. Equation (6)is the same at equation (1) but the dimension of each vector is Cinstead of 2. The output power can be expressed by relationship (7) asfollows:E[Y(k)² ]=E[W(k)^(H)X(k)X^(H)(k)W(k)]=W(k)^(H) R(k)W(k)  (7)where the correlation matrix R(k) is square with “C×C” dimensions. Thevector e is the steering vector describing the weights and delaysassociated with a desired monitoring direction and is of the formprovided by relationships (8) and (9) that follow: $\begin{matrix}{{e(\phi)} = {\frac{1}{C}\begin{bmatrix}1 & {\mathbb{e}}^{{+ {j\phi}}\quad k} & \ldots & {\mathbb{e}}^{{+ {j{({C - 1})}}}\phi\quad k}\end{bmatrix}}^{T}} & (8)\end{matrix}$  φ=(2πDfs/(cN))(sin(θ)) for k=0, 1, . . . , N−1  (9)where C is the number of array elements, c is the speed of sound inmeters per second, and θ is the desired “look direction.” Thus, vector emay be varied with frequency to change the desired monitoring directionor look-direction and correspondingly steer the array. With the sameconstraint regarding vector e as described by relationship (3), theproblem can be summarized by relationship (10) as follows:$\begin{matrix}{{\underset{W{(k)}}{Minimize}\quad\{ {{W(k)}^{H}{R(k)}{W(k)}} \}}{{such}\quad{that}}{{e^{H}{W(k)}} = 1}} & (10)\end{matrix}$This problem can be solved using the method of Lagrange multipliersgenerally characterized by relationship (11) as follows: $\begin{matrix}\begin{matrix}{{Minimize}\quad\{ {{CostFunction} + {\lambda*{Constraint}}} \}} \\w_{(k)}\end{matrix} & (11)\end{matrix}$where the cost function is the output power, and the constraint is aslisted above for vector e. A general vector solution begins with theLagrange multiplier function H(W) of relationship (12): $\begin{matrix}{{H(W)} = {{\frac{1}{2}{W(k)}^{H}{R(k)}{W(k)}} + {\lambda( {{e^{H}{W(k)}} - 1} )}}} & (12)\end{matrix}$where the factor of one half (½) is introduced to simplify later math.Taking the gradient of H(W) with respect to W(k), and setting thisresult equal to zero, relationship (13) results as follows:∇_(w) H(W)=R(k)W(k)+eλ=0  (13)Also, relationship (14) follows:W(k)=−R(k)⁻¹ eλ  (14)Using this result in the constraint equation relationships (15) and (16)that follow:e ^(H) └−R(k)⁻¹ eλ┘=1  (15)λ=−[e ^(H) R(k)⁻¹ e] ⁻¹  (16)and using relationship (14), the optimal weights are as set forth inrelationship (17):W _(opt) =R(k)⁻⁴ e[e ^(H) R(k)⁻¹ e] ⁻¹  (17)Because the bracketed term is a scalar, relationship (4) has this termin the denominator, and thus is equivalent.

Returning to the two variable case for the sake of clarity, relationship(5) may be expressed more compactly by absorbing the weighted sums intothe terms X_(ll), X_(lr), X_(rl) and X_(rr), and then renaming them ascomponents of the correlation matrix R(k) per relationship (18):$\begin{matrix}{{R(k)} = {\begin{bmatrix}{X_{ll}(k)} & {X_{lr}(k)} \\{X_{rl}(k)} & {X_{rr}(k)}\end{bmatrix} = \begin{bmatrix}R_{11} & R_{12} \\R_{21} & R_{22}\end{bmatrix}}} & (18)\end{matrix}$Its inverse may be expressed in relationship (19) as: $\begin{matrix}{{R(k)}^{- 1} = {\begin{bmatrix}R_{22} & {- R_{12}} \\{- R_{21}} & R_{11}\end{bmatrix}*\frac{1}{\det( {R(k)} )}}} & (19)\end{matrix}$where det( ) is the determinant operator. If the desired monitoringdirection is perpendicular to the sensor array, e=[0.5 0.5]^(T), thenumerator of relationship (4) may then be expressed by relationship (20)as: $\begin{matrix}{{{R(k)}^{- 1}\quad e} = {{\lbrack \quad\begin{matrix}R_{22} & {- R_{12}} \\{- R_{21}} & R_{11}\end{matrix} \rbrack\lbrack \quad\begin{matrix}0.5 \\0.5\end{matrix} \rbrack}*\quad{\frac{1}{\det( {R(k)} )}\lbrack \quad\begin{matrix}{R_{22} - R_{12}} \\{R_{11} - R_{21}}\end{matrix} \rbrack}*\quad\frac{0.5}{\det( {R(k)} )}}} & (20)\end{matrix}$Using the previous result, the denominator is expressed by relationship(21) as: $\begin{matrix}\begin{matrix}{{e^{H}{R(k)}^{- 1}e} = {\begin{bmatrix}0.5 & 0.5\end{bmatrix}*\begin{bmatrix}{R_{22} - R_{12}} \\{R_{11} - R_{21}}\end{bmatrix}*\frac{1}{\det( {R(k)} )}}} \\{= {( {R_{11} + R_{22} - R_{12} - R_{21}} )*\frac{0.5}{\det( {R(k)} )}}}\end{matrix} & (21)\end{matrix}$Canceling out the common factor of the determinant, the simplifiedrelationship (22) is completed as: $\begin{matrix}{\begin{bmatrix}w_{1} \\w_{2}\end{bmatrix} = {\frac{1}{( {R_{11} + R_{22} - R_{12} - R_{21}} )}*\begin{bmatrix}{R_{22} - R_{12}} \\{R_{11} - R_{21}}\end{bmatrix}}} & (22)\end{matrix}$It can also be expressed in terms of averages of the sums ofcorrelations between the two channels in relationship (23) as:$\begin{matrix}{\lbrack \quad\begin{matrix}{w_{l}(k)} \\{w_{r}(k)}\end{matrix} \rbrack = \quad{\frac{1}{( {{X_{ll}(k)} + {X_{rr}(k)} - {X_{lr}(k)} - {X_{rl}(k)}} )}*\lbrack \quad\begin{matrix}{{X_{rr}(k)} - {X_{lr}(k)}} \\{{X_{ll}(k)} - {X_{rl}(k)}}\end{matrix}\quad \rbrack}} & (23)\end{matrix}$where w_(l)(k) and w_(r)(k) are the desired weights for the left andright channels, 5 respectively, for the k^(th) frequency, and thecomponents of the correlation matrix are now expressed by relationships(24) as: $\begin{matrix}{{{X_{ll}(k)} = {\frac{M}{F}{\sum\limits_{n = 1}^{F}{{X_{l}^{*}( {n,k} )}{X_{l}( {n,k} )}}}}}{{X_{lr}(k)} = {\frac{1}{F}{\sum\limits_{n = 1}^{F}{{X_{l}^{*}( {n,k} )}{X_{r}( {n,k} )}}}}}{{X_{rl}(k)} = {\frac{1}{F}{\sum\limits_{n = 1}^{F}{{X_{r}^{*}( {n,k} )}{X_{l}( {n,k} )}}}}}{{X_{rr}(k)} = {\frac{M}{F}{\sum\limits_{n = 1}^{F}{{X_{r}^{*}( {n,k} )}{X_{r}( {n,k} )}}}}}} & (24)\end{matrix}$just as in relationship (5). Thus, after computing the averaged sums(which may be kept as running averages), computational load can bereduced for this two channel embodiment.

In a further variation of routine 140, a modified approach can beutilized in applications where gain differences between sensors of array20 are negligible. For this approach, an additional constraint isutilized. For a two-sensor arrangement with a fixed on-axis steeringdirection and negligible inter-sensor gain differences, the desiredweights satisfy relationship (25) as follows: $\begin{matrix}{{{Re}\lbrack w_{1} \rbrack} = {{{Re}\lbrack w_{2} \rbrack} = \frac{1}{2}}} & (25)\end{matrix}$The variance minimization goal and unity gain constraint for thisalternative approach correspond to the following relationships (26) and(27), respectively: $\begin{matrix}{\underset{W_{k}}{Min}E\{ {Y_{k}}^{2} \}} & (26) \\{{e^{H}\begin{bmatrix}{\frac{1}{2} + {{Im}\lbrack w_{1} \rbrack}} \\{\frac{1}{2} + {{Im}\lbrack w_{2} \rbrack}}\end{bmatrix}} = 1} & (27)\end{matrix}$By inspection, when e^(H)=[1 1], relationship (27) reduces torelationship (28) as follows:Im[w₁]=−Im[w₂]  (28)Solving for desired weights subject to the constraint in relationship(27) and using relationship (28) results in the following relationship(29): $\begin{matrix}{W_{opt} = {\begin{bmatrix}{1/2} \\{1/2}\end{bmatrix} + {{j\begin{bmatrix}{{Im}\lbrack R_{12} \rbrack} \\{- {{Im}\lbrack R_{12} \rbrack}}\end{bmatrix}} \cdot \frac{1}{{2{{Re}\lbrack R_{12} \rbrack}} - R_{11} - R_{22}}}}} & (29)\end{matrix}$

The weights determined in accordance with relationship (29) can be usedin place of those determined with relationships (22), (23), and (24);where R₁₁, R₁₂, R₂₁, R₂₂, are the same as those described in connectionwith relationship (18). Under appropriate conditions, this substitutiontypically provides comparable results with more efficient computation.When relationship (29) is utilized, it is generally desirable for thetarget speech or other acoustic signal to originate from the on-axisdirection and for the sensors to be matched to one another or tootherwise compensate for inter-sensor differences in gain.Alternatively, localization information about sources of interest ineach frequency band can be utilized to steer sensor array 20 inconjunction with the relationship (29) approach. This information can beprovided in accordance with procedure 520 more fully describedhereinafter in connection with the flowchart of FIG. 10.

Referring to relationship (5), regularization factor M typically isslightly greater than 1.00 to limit the magnitude of the weights in theevent that the correlation matrix R(k) is, or is close to being,singular, and therefore noninvertable. This occurs, for example, whentime-domain input signals are exactly the same for F consecutive FFTcalculations. It has been found that this form of regularization alsocan improve the perceived sound quality by reducing or eliminatingprocessing artifacts common to time-domain beamformers.

In one embodiment, regularization factor M is a constant. In otherembodiments, regularization factor M can be used to adjust or otherwisecontrol the array beamwidth, or the angular range at which a sound of aparticular frequency can impinge on the array relative to axis AZ and beprocessed by routine 140 without significant attenuation. This beamwidthis typically larger at lower frequencies than higher frequencies, andcan be expressed by the following relationship (30): $\begin{matrix}{{Beamwidth}_{{- 3}{dB}} = \quad{2 \cdot \quad{\sin^{- 1}( \quad\frac{{c \cdot \cos}\lfloor {1 + r + {\frac{r}{2}( {r - \sqrt{r^{2} + {4r} + 8}} )}} \rfloor}{2{\pi \cdot f \cdot D}}\quad )}}} & (30)\end{matrix}$r=1−M, where M is the regularization factor, as in relationship (5), crepresents the speed of sound in meters per second (m/s),f representsfrequency in Hertz (Hz), D is the distance between microphones in meters(m). For relationship (30), Beamwidth_(—3 dB) defines a beamwidth thatattenuates the signal of interest by a relative amount less than orequal to three decibels (dB). It should be understood that a differentattenuation threshold can be selected to define beamwidth in otherembodiments of the present invention. FIG. 9 provides a graph of fourlines of different patterns to represent constant values 1.001, 1.005,1.01, and 1.03, of regularization factor M, respectively, in terms ofbeamwidth versus frequency.

Per relationship (30), as frequency increases, beamwidth decreases; andas regularization factor M increases, the beamwidth increases.Accordingly, in one alternative embodiment of routine 140,regularization factor M is increased as a function of frequency toprovide a more uniform beamwidth across a desired range of frequencies.In another embodiment of routine 140, M is alternatively or additionallyvaried as a function of time. For example, if little interference ispresent in the input signals in certain frequency bands, theregularization factor M can be increased in those bands. It has beenfound that beamwidth increases in frequency bands with low or noinference commonly provide a better subjective sound quality by limitingthe magnitude of the weights used in relationships (22), (23), and/or(29). In a further variation, this improvement can be complemented bydecreasing regularization factor M for frequency bands that containinterference above a selected threshold. It has been found that suchdecreases commonly provide more accurate filtering, and bettercancellation of interference. In still another embodiment,regularization factor M varies in accordance with an adaptive functionbased on frequency-band-specific interference. In yet furtherembodiments, regularization factor M varies in accordance with one ormore other relationships as would occur to those skilled in the art.

Referring to FIG. 4, one application of the various embodiments of thepresent invention is depicted as hearing aid system 210; where likereference numerals refer to like features. In one embodiment, system 210includes eyeglasses G and acoustic sensors 22 and 24. Acoustic sensors22 and 24 are fixed to eyeglasses G in this embodiment and spaced apartfrom one another, and are operatively coupled to processor 30. Processor30 is operatively coupled to output device 190. Output device 190 is inthe form of a hearing aid earphone and is positioned in ear E of theuser to provide a corresponding audio signal. For system 210, processor30 is configured to perform routine 140 or its variants with the outputsignal y(t) being provided to output device 190 instead of output device90 of FIG. 2. As previously discussed, an additional output device 190can be coupled to processor 30 to provide sound to another ear (notshown). This arrangement defines axis AZ to be perpendicular to the viewplane of FIG. 4 as designated by the like labeled cross-hairs locatedgenerally midway between sensors 22 and 24.

In operation, the user wearing eyeglasses G can selectively receive anacoustic signal by aligning the corresponding source with a designateddirection, such as axis AZ. As a result, sources from other directionsare attenuated. Moreover, the wearer may select a different signal byrealigning axis AZ with another desired sound source and correspondinglysuppress a different set of off-axis sources. Alternatively oradditionally, system 210 can be configured to operate with a receptiondirection that is not coincident with axis AZ.

Processor 30 and output device 190 may be separate units (as depicted)or included in a common unit worn in the ear. The coupling betweenprocessor 30 and output device 190 may be an electrical cable or awireless transmission. In one alternative embodiment, sensors 22, 24 andprocessor 30 are remotely located relative to each other and areconfigured to broadcast to one or more output devices 190 situated inthe ear E via a radio frequency transmission.

In a further hearing aid embodiment, sensors 22, 24 are sized and shapedto fit in the ear of a listener, and the processor algorithms areadjusted to account for shadowing caused by the head, torso, and pinnae.This adjustment may be provided by deriving aHead-Related-Transfer-Function (HRTF) specific to the listener or from apopulation average using techniques known to those skilled in the art.This function is then used to provide appropriate weightings of theoutput signals that compensate for shadowing.

Another hearing aid system embodiment is based on a cochlear implant. Acochlear implant is typically disposed in a middle ear passage of a userand is configured to provide electrical stimulation signals along themiddle ear in a standard manner. The implant can include some or all ofprocessing subsystem 30 to operate in accordance with the teachings ofthe present invention. Alternatively or additionally, one or moreexternal modules include some or all of subsystem 30. Typically a sensorarray associated with a hearing aid system based on a cochlear implantis worn externally, being arranged to communicate with the implantthrough wires, cables, and/or by using a wireless technique.

Besides various forms of hearing aids, the present invention can beapplied in other configurations. For instance, FIG. 5 shows a voiceinput device 310 employing the present invention as a front end speechenhancement device for a voice recognition routine for personal computerC; where like reference numerals refer to like features. Device 310includes acoustic sensors 22, 24 spaced apart from each other in apredetermined relationship. Sensors 22, 24 are operatively coupled toprocessor 330 within computer C. Processor .330 provides an outputsignal for internal use or responsive reply via speakers 394 a, 394 band/or visual display 396; and is arranged to process vocal inputs fromsensors 22, 24 in accordance with routine 140 or its variants. In onemode of operation, a user of computer C aligns with a predetermined axisto deliver voice inputs to device 310. In another mode of operation,device 310 changes its monitoring direction based on feedback from anoperator and/or automatically selects a monitoring direction based onthe location of the most intense sound source over a selected period oftime. Alternatively or additionally, the source localization/trackingability provided by procedure 520 as illustrated in the flowchart ofFIG. 10 can be utilized. In still another voice input application, thedirectionally selective speech processing features of the presentinvention are utilized to enhance performance of a hands-free telephone,audio surveillance device, or other audio system.

Under certain circumstances, the directional orientation of a sensorarray relative to the target acoustic source changes. Without accountingfor such changes, attenuation of the target signal can result. Thissituation can arise, for example, when a binaural hearing aid wearerturns his or her head so that he or she is not aligned properly with thetarget source, and the hearing aid does not otherwise account for thismisalignment. It has been found that attenuation due to misalignment canbe reduced by localizing and/or tracking one or more acoustic sources ofinterests. The flowchart of FIG. 10 illustrates procedure 520 to trackand/or localize a desired acoustic source relative to a reference.Procedure 520 can be utilized for a hearing aid or in other applicationssuch as a voice input device, a hands-free telephone, audio surveillanceequipment, and the like—either in conjunction with or independent ofpreviously described embodiments. Procedure 520 is described as followsin terms of an implementation with system 10 of FIG. 1. For thisembodiment, processing system 30 can include logic to execute one ormore stages and/or conditionals of procedure 520 as appropriate. Inother embodiments, a different arrangement can be used to implementprocedure 520 as would occur to one skilled in the art.

Procedure 520 starts with AID conversion in stage 522 in a manner likethat described for stage 142 of routine 140. From stage 522, procedure520 continues with stage 524 to transform the digital data obtained fromstage 522, such that “G” number of FFTs are provided each with “N”number of FFT frequency bins. Stages 522 and 524 can be executed in anongoing fashion, buffering the results periodically for later access byother operations of procedure 520 in a parallel, pipelined,sequence-specific, or different manner as would occur to one skilled inthe art. With the FFTs from stage 524, an array of localization results,P(γ), can be described in terms of relationships (31)-(35) as follows:$\begin{matrix}{{{P(\gamma)} = {\sum\limits_{g = 1}^{G}( {\sum\limits_{k = 0}^{{N/2} - 1}{\sum\limits_{n}{d( \theta_{x} )}}} )}},{\gamma = \lbrack {{{- 90}{^\circ}},{{- 89}{^\circ}},{{- 88}{^\circ}},\ldots\quad,{89{^\circ}},{90{^\circ}}} \rbrack}} & (31) \\{n = \lbrack {0,\ldots\quad,{{INT}( \frac{D \cdot f_{s}}{c} )}} \rbrack} & (32) \\\begin{matrix}{{{d( \theta_{x} )} = 1},} & {\theta_{x} \in {\gamma\quad{and}}} \\ & {{{x( {g,k} )}} \leq {1\quad{and}}} \\ & {{{{L( {g,k} )}} + {{R( {g,k} )}}} \geq {M_{thr}(k)}} \\{{= 0},} & {\theta_{x} \notin {\gamma\quad{or}}} \\ & {{{x( {g,k} )}} > {1\quad{or}}} \\ & {{{{L( {g,k} )}} + {{R( {g,k} )}}} < {M_{thr}(k)}}\end{matrix} & (33) \\{\theta_{x} = {{ROUND}\quad( {\sin^{- 1}( {x( {g,k} )} )} )}} & (34) \\{{x( {g,k} )} = {\frac{N \cdot c}{2{\pi \cdot k \cdot f_{s} \cdot D}}( {{\angle\quad{L( {g,k} )}} - {{\angle\quad{R( {g,k} )}} \pm {2\pi\quad n}}} )}} & (35)\end{matrix}$where the operator “INT” returns the integer part of its operand, L(g,k)and R(g,k) are the frequency-domain data from channels L and R,respectively, for the k^(th) FFT frequency bin of the g^(th) FFT,M^(thr)(k) is a threshold value for the frequency-domain data in FFTfrequency bin k, the operator “ROUND” returns the nearest integer degreeof its operand, c is the speed of sound in meters per second, f_(s) isthe sampling rate in Hertz, and D is the distance (in meters) betweenthe two sensors of array 20. For these relationships, array P(γ) isdefined with 181 azimuth location elements, which correspond todirections −90° to +90° in 1° increments. In other embodiments, adifferent resolution and/or location indication technique can be used.

From stage 524, procedure 520 continues with index initialization stage526 in which index g to the G number of FFTs and index k to the Nfrequency bins of each FFT are set to one and zero, (g=1, k=0),respectively. From stage 526, procedure 520 continues by enteringfrequency bin processing loop 530 and FFT processing loop 540. For thisexample, loop 530 is nested within loop 540. Loops 530 and 540 beginwith stage 532.

For an off-axis acoustic source, the corresponding signal travelsdifferent distances to reach each of the sensors 22, 24 of array 20.Generally, these different distances cause a phase difference betweenchannels L and R at some frequency. In stage 532, routine 520 determinesthe difference in phase between channels L and R for the currentfrequency bin k of the FFT g, converts the phase difference to adifference in distance, and determines the ratio x(g,k) of this distancedifference to the sensor spacing D in accordance with relationship (35).Ratio x(g,k) is used to find the signal angle of arrival θ_(x), roundedto the nearest degree, in accordance with relationship (34).

Conditional 534 is next encountered to test whether the signal energylevel in channels L and R have more energy than a threshold levelM_(thr) and the value of x(g,k) was one for which a valid angle ofarrival could be calculated. If both conditions are met, then in stage535 a value of one is added to the corresponding element of P(γ), wherey=θ_(x). Procedure 520 proceeds from stage 535 to conditional 536. Ifneither condition of conditional 534 is met, then P(γ) is not modified,and procedure 520 bypasses stage 535, continuing with conditional 536.

Conditional 536 tests if all the frequency bins have been processed,that is whether index k equals N, the total number of bins. If not(conditional 536 test is negative), procedure 520 continues with stage537 in which index k is incremented by one (k=k+1). From stage 537, loop530 closes, returning to stage 532 to process the new g and kcombination. If the conditional 536 test is affirmative, conditional 542is next encountered, which tests if all FFTs have been processed, thatis whether index g equals G number of FFTs. If not (conditional 542 isnegative), procedure 520 continues with stage 544 to increment g by one(g=g+1) and to reset k to zero (k=0). From stage 544, loop 540 closes,returning to stage 532 to process the new g and k combination. Ifconditional test 542 is affirmative, then all N bins for each of the Gnumber of FFTs have been processed, and loops 530 and 540 are exited.

With the conclusion of processing by loops 530 and 540, the elements ofarray P(γ) provide a measure of the likelihood that an acoustic sourcecorresponds to a given direction (azimuth in this case). By examiningP(γ), an estimate of the spatial distribution of acoustic sources at agiven moment in time is obtained. From loops 530, 540, procedure 520continues with stage 550.

In stage 550, the elements of array P(γ) having the greatest relativevalues, or “peaks,” are identified in accordance with relationship (36)as follows:p(l)=PEAKS(P(γ), γ_(lim), P_(thr))  (36)where p(l) is direction of the l^(th) peak in the function P(γ) forvalues of γ between ±γ_(lim) (a typical value for γ_(lim) is 10°, butthis may vary significantly) and for which the peak values are above thethreshold value P_(thr). The PEAKS operation of relationship (36) canuse a number of peak-finding algorithms to locate maxima of the data,including optionally smoothing the data and other operations.

From stage 550, procedure 520 continues with stage 552 in which one ormore peaks are selected. When tracking a source that was initiallyon-axis, the peak closest to the on-axis direction typically correspondsto the desired source. The selection of this closest peak can beperformed in accordance with relationship (37) as follows:$\begin{matrix}{\theta_{tar} = {\min\limits_{l}{{p(l)}}}} & (37)\end{matrix}$where θ_(tar) is the direction angle of the chosen peak. Regardless ofthe selection criteria, procedure 520 proceeds to stage 554 to apply theselected peak or peaks. Procedure 520 continues from stage 554 toconditional 560. Conditional 560 tests whether procedure 520 is tocontinue or not. If the conditional 560 test is true, procedure 520loops back to stage 522. If the conditional 560 test is false, procedure520 halts.

In an application relating to routine 140, the peak closest to axis AZis selected, and utilized to steer array 20 by adjusting steering vectore. In this application, vector e is modified for each frequency bin k sothat it corresponds to the closest peak direction θ_(tar). For asteering direction of θ_(tar), the vector e can be represented by thefollowing relationship (38), which is a simplified version ofrelationships (8) and (9): $\begin{matrix}{{e = \begin{bmatrix}1 & e^{{+ {j\phi}}\quad k}\end{bmatrix}^{T}}{\phi = ( {\frac{2{\pi \cdot D \cdot f_{s}}}{c \cdot N} \cdot {\sin( \theta_{tar} )}} )}} & (38)\end{matrix}$where k is the FFT frequency bin number, D is the distance in metersbetween sensors 22 and 24, f_(s) is the sampling frequency in Hertz, cis the speed of sound in meters per second, N is the number of FFTfrequency bins and θ_(tar) is obtained from relationship (37). Forroutine 140, the modified steering vector e of relationship (38) can besubstituted into relationship (4) of routine 140 to extract a signaloriginating from direction θ_(tar). Likewise, procedure 520 can beintegrated with routine 140 to perform localization with the same FFTdata. In other words, the AID conversion of stage 142 can be used toprovide digital data for subsequent processing by both routine 140 andprocedure 520. Alternatively or additionally, some or all of the FFTsobtained for routine 140 can be used to provide the G FFTs for procedure520. Moreover, beamwidth modifications can be combined with procedure520 in various applications either with or without routine 140. In stillother embodiments, the indexed execution of loops 530 and 540 can be atleast partially performed in parallel with or without routine 140.

In a further embodiment, one or more transformation techniques areutilized in addition to or as an alternative to fourier transforms inone or more forms of the invention previously described. One example isthe wavelet transform, which mathematically breaks up the time-domainwaveform into many simple waveforms, which may vary widely in shape.Typically wavelet basis functions are similarly shaped signals withlogarithmically spaced frequencies. As frequency rises, the basisfunctions become shorter in time duration with the inverse of frequency.Like fourier transforms, wavelet transforms represent the processedsignal with several different components that retain amplitude and phaseinformation. Accordingly, routine 140 and/or routine 520 can be adaptedto use such alternative or additional transformation techniques. Ingeneral, any signal transform components that provide amplitude and/orphase information about different parts of an input signal and have acorresponding inverse transformation can be applied in addition to or inplace of FFTs.

Routine 140 and the variations previously described generally adapt morequickly to signal changes than conventional time-domainiterative-adaptive schemes. In certain applications where the inputsignal changes rapidly over a small interval of time, it may be desiredto be more responsive to such changes. For these applications, the Fnumber of FFTs associated with correlation matrix R(k) may provide amore desirable result if it is not constant for all signals(alternatively designated the correlation length F). Generally, asmaller correlation length F is best for rapidly changing input signals,while a larger correlation length F is best for slowly changing inputsignals.

A varying correlation length F can be implemented in a number of ways.In one example, filter weights are determined using different parts ofthe frequency-domain data stored in the correlation buffers. For bufferstorage in the order of the time they are obtained (First-In, First-Out(FIFO) storage), the first half of the correlation buffer contains dataobtained from the first half of the subject time interval and the secondhalf of the buffer contains data from the second half of this timeinterval. Accordingly, the correlation matrices R₁(k) and R₂(k) can bedetermined for each buffer half according to relationships (39) and (40)as follows: $\begin{matrix}{{R_{1}(k)} = \begin{bmatrix}{\frac{2M}{F}{\sum\limits_{n = 1}^{\frac{F}{2}}\quad{{X_{l}^{*}( {n,k} )}{X_{l}( {n,k} )}}}} & {\frac{2}{F}{\sum\limits_{n = 1}^{\frac{F}{2}}\quad{{X_{l}^{*}( {n,k} )}{X_{r}( {n,k} )}}}} \\{\frac{2}{F}{\sum\limits_{n = 1}^{\frac{F}{2}}\quad{{X_{r}^{*}( {n,k} )}{X_{l}( {n,k} )}}}} & {\frac{2M}{F}{\sum\limits_{n = 1}^{\frac{F}{2}}\quad{{X_{r}^{*}( {n,k} )}{X_{r}( {n,k} )}}}}\end{bmatrix}} & (39) \\{{R_{2}(k)} = \begin{bmatrix}{\frac{2M}{F}{\sum\limits_{n = {\frac{F}{2} + 1}}^{F}\quad{{X_{l}^{*}( {n,k} )}{X_{l}( {n,k} )}}}} & {\frac{2}{F}{\sum\limits_{n = {\frac{F}{2} + 1}}^{F}\quad{{X_{l}^{*}( {n,k} )}{X_{r}( {n,k} )}}}} \\{\frac{2}{F}{\sum\limits_{n = {\frac{F}{2} + 1}}^{F}\quad{{X_{r}^{*}( {n,k} )}{X_{l}( {n,k} )}}}} & {\frac{2M}{F}{\sum\limits_{n = {\frac{F}{2} + 1}}^{F}\quad{{X_{r}^{*}( {n,k} )}{X_{r}( {n,k} )}}}}\end{bmatrix}} & (40)\end{matrix}$R(k) can be obtained by summing correlation matrices R₁(k) and R₂(k).

Using relationship (4) of routine 140, filter coefficients (weights) canbe obtained using both R₁(k) and R₂(k). If the weights differsignificantly for some frequency band k between R₁(k) and R₂(k), asignificant change in signal statistics may be indicated. This changecan be quantified by examining the change in one weight throughdetermining the magnitude and phase change of the weight and then usingthese quantities in a function to select the appropriate correlationlength F. The magnitude difference is defined according to relationship(41) as follows:ΔM(k)=||w _(1.1)(k)|−|w_(1.2)(k)||  (41)where w_(1.1)(k) and w_(1.2)(k) are the weights calculated for the leftchannel using R₁(k) and R₂(k), respectively. The angle difference isdefined according to relationship (42) as follows: $\begin{matrix}\begin{matrix}{{\Delta\quad{A(k)}} = {{\min( {{a_{1} - {\angle\quad{w_{\angle 2}(k)}}},\quad{a_{2} - {\angle\quad{w_{\angle 2}(k)}}},\quad{a_{3} - {\angle\quad{w_{\angle 2}(k)}}}} )}}} \\{a_{1} = {\angle\quad{w_{\angle 1}(k)}}} \\{a_{2} = {{\angle\quad{w_{\angle 1}(k)}} + {2\pi}}} \\{a_{3} = {{\angle\quad{w_{\angle 1}(k)}} - {2\pi}}}\end{matrix} & (42)\end{matrix}$where the factor of ±2π is introduced to provide the actual phasedifference in the case of a ±2π jump in the phase of one of the angles.

The correlation length F for some frequency bin k is now denoted asF(k). An example function is given by the following relationship (43):F(k)=max(b(k)·ΔA(k)+d(k)·ΔM(k)+c _(max)(k), C _(min)(k))  (43)where C_(min)(k) represents the minimum correlation length, c_(max)(k)represents the maximum correlation length and b(k) and d(k) are negativeconstants, all for the k^(th) frequency band. Thus, as ΔA(k) and ΔM(k)increase, indicating a change in the data, the output of the functiondecreases. With proper choice of b(k) and d(k), F(k) is limited betweenc_(min)(k) and c_(min)(k), so that the correlation length can vary onlywithin a predetermined range. It should also be understood that F(k) maytake different forms, such as a nonlinear function or a function ofother measures of the input signals.

Values for function F(k) are obtained for each frequency bin k. It ispossible that a small number of correlation lengths may be used, so ineach frequency bin k the correlation length that is closest to F₁(k) isused to form R(k). This closest value is found using relationship (44)as follows:$\begin{matrix}{\quad\begin{matrix}{{i_{\min} = {\min\limits_{i}( {{{F_{1}(k)} - {c(i)}}} )}},} \\{{c(i)} = \lbrack {c_{\min},c_{2},c_{3},\ldots\quad,c_{\max}} \rbrack} \\{{F(k)} = {c( i_{\min} )}}\end{matrix}} & (44)\end{matrix}$where i_(min), is the index for the minimized function F(k) and c(i) isthe set of possible correlation length values ranging from c_(min) toc_(max).

The adaptive correlation length process described in connection withrelationships (39)-(44) can be incorporated into the correlation matrixstage 162 and weight determination stage 164 for use in a hearing aid,such as that described in connection with FIG. 4, or other applicationslike surveillance equipment, voice recognition systems, and hands-freetelephones, just to name a few. Logic of processing subsystem 30 can beadjusted as appropriate to provide for this incorporation. Optionally,the adaptive correlation length process can be utilized with therelationship (29) approach to weight computation, the dynamic beamwidthregularization factor variation described in connection withrelationship (30) and FIG. 9, the localization/tracking procedure 520,alternative transformation embodiments, and/or such differentembodiments or variations of routine 140 as would occur to one skilledin the art. The application of adaptive correlation length can beoperator selected and/or automatically applied based on one or moremeasured parameters as would occur to those skilled in the art.

Many other further embodiments of the present invention are envisioned.One further embodiment includes: detecting acoustic excitation with anumber of acoustic sensors that provide a number of sensor signals;establishing a set of frequency components for each of the sensorsignals; and determining an output signal representative of the acousticexcitation from a designated direction. This determination includesweighting the set of frequency components for each of the sensor signalsto reduce variance of the output signal and provide a predefined gain ofthe acoustic excitation from the designated direction.

In another embodiment, a hearing aid includes a number of acousticsensors in the presence of multiple acoustic sources that provide acorresponding number of sensor signals. A selected one of the acousticsources is monitored. An output signal representative of the selectedone of the acoustic sources is generated. This output signal is aweighted combination of the sensor signals that is calculated tominimize variance of the output signal.

A still further embodiment includes: operating a voice input deviceincluding a number of acoustic sensors that provide a correspondingnumber of sensor signals; determining a set of frequency components foreach of the sensor signals; and generating an output signalrepresentative of acoustic excitation from a designated direction. Thisoutput signal is a weighted combination of the set of frequencycomponents for each of the sensor signals calculated to minimizevariance of the output signal.

Yet a further embodiment includes an acoustic sensor array operable todetect acoustic excitation that includes two or more acoustic sensorseach operable to provide a respective one of a number of sensor signals.Also included is a processor to determine a set of frequency componentsfor each of the sensor signals and generate an output signalrepresentative of the acoustic excitation from a designated direction.This output signal is calculated from a weighted combination of the setof frequency components for each of the sensor signals to reducevariance of the output signal subject to a gain constraint for theacoustic excitation from the designated direction.

A further embodiment includes: detecting acoustic excitation with anumber of acoustic sensors that provide a corresponding number ofsignals; establishing a number of signal transform components for eachof these signals; and determining an output signal representative ofacoustic excitation from a designated direction. The signal transformcomponents can be of the frequency domain type. Alternatively oradditionally, a determination of the output signal can include weightingthe components to reduce variance of the output signal and provide apredefined gain of the acoustic excitation from the designateddirection.

In yet another embodiment, a hearing aid is operated that includes anumber of acoustic sensors. These sensors provide a corresponding numberof sensor signals. A direction is selected to monitor for acousticexcitation with the hearing aid. A set of signal transform componentsfor each of the sensor signals is determined and a number of weightvalues are calculated as a function of a correlation of thesecomponents, an adjustment factor, and the selected direction. The signaltransform components are weighted with the weight values to provide anoutput signal representative of the acoustic excitation emanating fromthe direction. The adjustment factor can be directed to correlationlength or a beamwidth control parameter just to name a few examples.

For a further embodiment, a hearing aid is operated that includes anumber of acoustic sensors to provide a corresponding number of sensorsignals. A set of signal transform components are provided for each ofthe sensor signals and a number of weight values are calculated as afunction of a correlation of the transform components for each of anumber of different frequencies. This calculation includes applying afirst beamwidth control value for a first one of the frequencies and asecond beamwidth control value for a second one of the frequencies thatis different than the first value. The signal transform components areweighted with the weight values to provide an output signal.

For another embodiment, acoustic sensors of the hearing aid providecorresponding signals that are represented by a plurality of signaltransform components. A first set of weight values are calculated as afunction of a first correlation of a first number of these componentsthat correspond to a first correlation length. A second set of weightvalues are calculated as a function of a second correlation of a secondnumber of these components that correspond to a second correlationlength different than the first correlation length. An output signal isgenerated as a function of the first and second weight values.

In another embodiment, acoustic excitation is detected with a number ofsensors that provide a corresponding number of sensor signals. A set ofsignal transform components is determined for each of these signals. Atleast one acoustic source is localized as a function of the transformcomponents. In one form of this embodiment, the location of one or moreacoustic sources can be tracked relative to a reference. Alternativelyor additionally, an output signal can be provided as a function of thelocation of the acoustic source determined by localization and/ortracking, and a correlation of the transform components.

It is contemplated that various signal flow operators, converters,functional blocks, generators, units, stages, processes, and techniquesmay be altered, rearranged, substituted, deleted, duplicated, combinedor added as would occur to those skilled in the art without departingfrom the spirit of the present inventions. It should be understood thatthe operations of any routine, procedure, or variant thereof can beexecuted in parallel, in a pipeline manner, in a specific sequence, as acombination of these appropriate to the interdependence of suchoperations on one another, or as would otherwise occur to those skilledin the art. By way of nonlimiting example, A/D conversion, D/Aconversion, FFT generation, and FFT inversion can typically be performedas other operations are being executed. These other operations could bedirected to processing of previously stored AID or signal transformcomponents, such as stages 150, 162, 164, 532, 535, 550, 552, and 554,just to name a few possibilities. In another nonlimiting example, thecalculation of weights based on the current input signal can at leastoverlap the application of previously determined weights to a signalabout to be output. All publications and patent applications cited inthis specification are herein incorporated by reference as if eachindividual publication or patent application were specifically andindividually indicated to be incorporated by reference.

EXPERIMENTAL SECTION

The following experimental results provide nonlimiting examples, andshould not be construed to restrict the scope of the present invention.

FIG. 6 illustrates the experimental set-up for testing the presentinvention. The algorithm has been tested with real recorded speechsignals, played through loudspeakers at different spatial locationsrelative to the receiving microphones in an anechoic chamber. A pair ofmicrophones 422, 424 (Sennheiser MKE 2-60) with an inter-microphonedistance D of 15 cm, were situated in a listening room to serve assensors 22, 24 . Various loudspeakers were placed at a distance of about3 feet from the midpoint M of the microphones 422, 424 corresponding todifferent azimuths. One loudspeaker was situated in front of themicrophones that intersected axis AZ to broadcast a target speech signal(corresponding to source 12 of FIG. 2). Several loudspeakers were usedto broadcast words or sentences that interfere with the listening oftarget speech from different azimuths.

Microphones 422, 424 were each operatively coupled to a Mic-to-Linepreamp 432 (Shure FP-11). The output of each preamp 432 was provided toa dual channel volume control 434 provided in the form of an audiopreamplifier (Adcom GTP-5511). The output of volume control 434 was fedinto A/D converters of a Digital Signal Processor (DSP) developmentboard 440 provided by Texas Instruments (model number TI-C6201 DSPEvaluation Module (EVM)). Development board 440 includes a fixed-pointDSP chip (model number TMS320C62) running at a clock speed of 133 MHzwith a peak throughput of 1064 MIPS (millions of instructions persecond). This DSP executed software configured to implement routine 140in real-time. The sampling frequency for these experiments was about 8kHz with 16-bit A/D and D/A conversion. The FFT length was 256 samples,with an FFT calculated every 16 samples. The computation leading to thecharacterization and extraction of the desired signal was found tointroduce a delay in a range of about 10-20 milliseconds between theinput and output.

FIGS. 7 and 8 each depict traces of three acoustic signals ofapproximately the same energy. In FIG. 7, the target signal trace isshown between two interfering signals traces broadcast from azimuths 22°and −65°, respectively. These azimuths are depicted in FIG. 1. Thetarget sound is a prerecorded voice from a female (second trace), and isemitted by the loudspeaker located near 0°. One interfering sound isprovided by a female talker (top trace of FIG. 7) and the otherinterfering sound is provided by a male talker (bottom trace of FIG. 7).The phrase repeated by the corresponding talker is reproduced above therespective trace.

Referring to FIG. 8, as revealed by the top trace, when the targetspeech sound is emitted in the presence of two interfering sources, itswaveform (and power spectrum) is contaminated. This contaminated soundwas difficult to understand for most listeners, especially those withhearing impairment. Routine 140, as embodied in board 440, processedthis contaminated signal with high fidelity and extracted the targetsignal by markedly suppressing the interfering sounds. Accordingly,intelligibility of the target signal was restored as illustrated by thesecond trace. The intelligibility was significantly improved and theextracted signal resembled the original target signal reproduced forcomparative purposes as the bottom trace of FIG. 8.

These experiments demonstrate marked suppression of interfering sounds.The use of the regularization parameter (valued at approximately 1.03)effectively limited the magnitude of the calculated weights and resultsin an output with much less audible distortion when the target source isslightly off-axis, as would occur when the hearing aid wearer's head isslightly misaligned to the target talker. Miniaturization of thistechnology to a size suitable for hearing aids and other applicationscan be provided using techniques known to those skilled in the art.

FIGS. 11 and 12 are computer generated image graphs of simulated resultsfor procedure 520. These graphs plot localization results of azimuth indegrees versus time in seconds. The localization results are plotted asshading, where the darker the shading, the stronger the localizationresult at that angle and time. Such simulations are accepted by thoseskilled in the art to indicate efficacy of this type of procedure.

FIG. 11 illustrates the localization results when the target acousticsource is generally stationary with a direction of about 10° off-axis.The actual direction of the target is indicated by a solid black line.FIG. 12 illustrates the localization results for a target with adirection that is changing sinusoidally between +10° and −10°, as mightbe the case for a hearing aid wearer shaking his or her head. The actuallocation of the source is again indicated by a solid black line. Thelocalization technique of procedure 520 accurately indicates thelocation of the target source in both cases because the darker shadingmatches closely to the actual location lines. Because the target sourceis not always producing a signal free of interference overlap,localization results may be strong only at certain times. In FIG. 12,these stronger intervals can be noted at about 0.2, 0.7, 0.9, 1.25, 1.7,and 2.0 seconds. It should be understood that the target location can bereadily estimated between such times.

Experiments described herein are simply for the purpose of demonstratingoperation of one form of a processing system of the present invention.The equipment, the speech materials, the talker configurations, and/orthe parameters can be varied as would occur to those skilled in the art.

Any theory, mechanism of operation, proof, or finding stated herein ismeant to further enhance understanding of the present invention and isnot intended to make the present invention in any way dependent uponsuch theory, mechanism of operation, proof, or finding. While theinvention has been illustrated and described in detail in the drawingsand foregoing description, the same is to be considered as illustrativeand not restrictive in character, it being understood that only theselected embodiments have been shown and described and that all changes,modifications and equivalents that come within the spirit of theinvention as defined herein or by the following claims are desired to beprotected.

1. A method, comprising: detecting acoustic excitation with a number ofacoustic sensors, the acoustic sensors providing a corresponding numberof sensor signals; establishing a number of frequency domain componentsfor each of the sensor signals; and determining an output signalrepresentative of the acoustic excitation from a designated direction,said determining including weighting the components for each of thesensor signals to reduce variance of the output signal and provide apredefined gain of the acoustic excitation from the designateddirection.
 2. The method of claim 1, wherein said determining includesminimizing the variance of the output signal and the predefined gain isapproximately unity.
 3. The method of claim 1, further comprisingchanging the designated direction without moving any of the acousticsensors and repeating said establishing and said determining after saidchanging.
 4. The method of claim 1, further comprising changing from thedesignated direction by moving one or more of the acoustic sensors andrepeating said establishing and said determining after said changing. 5.The method of claim 1, wherein said components correspond to fouriertransforms and said weighting includes calculating a number of weightsto minimize the variance of the output signal subject to a constraintthat the predefined gain be generally maintained at unity, the weightsbeing determined as a function of a frequency domain correlation matrixand a vector corresponding to the designated direction.
 6. The method ofclaim 5, further comprising recalculating the weights from time to timeand repeating said establishing and said determining on an establishedbasis.
 7. The method of claim 1, further comprising calculating saidweights subject to a constraint of an insubstantial level of gaindifference between the acoustic sensors.
 8. The method of claim 1,further comprising adjusting a correlation factor to control beamwidthas a function of frequency.
 9. The method of claim 1, further comprisingcalculating a number of correlation matrices and adaptively changingcorrelation length for one or more of the correlation matrices relativeto at least one other of the correlation matrices.
 10. The method ofclaim 1, further comprising tracking location of at least one acousticsignal source as a function of a phase difference between the acousticsensors.
 11. The method of any of claims 1-10, further comprisingproviding a hearing aid with the acoustic sensors and a processoroperable to perform said establishing and said determining.
 12. Themethod of any of claims 1-10, wherein a voice input device includes theacoustic sensors and a processor operable to perform said establishingand said determining.
 13. A method, comprising: operating a hearing aidincluding a number of acoustic sensors in the presence of multipleacoustic sources, the acoustic sensors providing a corresponding numberof sensor signals; monitoring a selected one of the acoustic sources;determining a set of frequency components for each of the sensorsignals; and generating an output signal representative of the selectedone of the acoustic sources, the output signal being a weightedcombination of the set of frequency components for each of the sensorsignals calculated to minimize variance of the output signal.
 14. Themethod of claim 13, further comprising processing the output signal toprovide at least one acoustic output to a user of the hearing aid.
 15. Amethod, comprising: operating a voice input device including a number ofacoustic sensors, the acoustic sensors providing a corresponding numberof sensor signals; determining a set of frequency components for each ofthe sensor signals; and generating an output signal representative ofacoustic excitation from a designated direction, the output signal beinga weighted combination of the set of frequency components for each ofthe sensor signals calculated to minimize variance of the output signal.16. The method of claim 15, wherein the voice input device is includedin a voice recognition system for a computer.
 17. The method of any ofclaims 13-16, wherein said generating includes calculating a number ofweights as a function of a frequency domain correlation matrix and avector corresponding to the designated direction.
 18. The method ofclaim 17, further comprising recalculating the weights from time totime.
 19. The method of claim 17, further comprising determining theweighted combination of the sensor signals as a function of a gainconstraint associated with the designated direction.
 20. The method ofclaim 17, further comprising adjusting a correlation factor to controlbeamwidth as a function of frequency.
 21. The method of claim 17,further comprising adaptively changing correlation length.
 22. A method,comprising: operating a hearing aid including a number of acousticsensors, the acoustic sensors providing a corresponding number of sensorsignals; selecting a direction to monitor for acoustic excitation withthe hearing aid; determining a set of signal transform components foreach of the sensor signals; calculating a number of weight values as afunction of a correlation of the signal transform components, anadjustment factor, and the direction; and weighting the signal transformcomponents with the weight values to provide an output signalrepresentative of the acoustic excitation emanating from the direction.23. The method of claim 22, wherein the transform components correspondto different frequencies and the adjustment factor has a first value fora first one of the frequencies and second value different than the firstvalue for a second one of the frequencies to control beamwidth.
 24. Themethod of claim 22, wherein the adjustment factor corresponds tocorrelation length and further comprising determining a number ofdifferent correlations with correlation length adaptively changed inaccordance with different values for the adjustment factor.
 25. Themethod of claim 22, further comprising: determining a level ofinterference; and adjusting the beamwidth of the hearing aid in responseto the level of interference with the adjustment factor.
 26. The methodof claim 22, further comprising: determining a rate of change of atleast one frequency of at least one of the sensor signals with respectto time; and adjusting the correlation length in response to the rate ofchange with the adjustment factor.
 27. A method, comprising: operating ahearing aid including a number of acoustic sensors, the acoustic sensorsproviding a corresponding number of sensor signals; providing a set ofsignal transform components for each of the sensor signals; calculatinga number of weight values as a function of a correlation of thetransform components for each of a number different frequencies, saidcalculating including applying a first beamwidth control value for afirst one of the frequencies and a second beamwidth control value for asecond one of the frequencies different than the first beamwidth controlvalue; and weighting the signal transform components with the weightvalues to provide an output signal.
 28. The method of claim 27, furthercomprising selecting the first beamwidth value and the second beamwidthvalue to provide a generally constant beamwidth of the hearing aid overa predefined frequency range.
 29. The method of claim 27, wherein thefirst beamwidth value and the second beamwidth value differ inaccordance with a difference in an amount of interference at the firstone of the frequencies relative to the second one of the frequencies.30. A method, comprising: operating a hearing aid including a number ofacoustic sensors, the acoustic sensors providing a corresponding numberof sensor signals; providing a first plurality of signal transformcomponents for the sensor signals; calculating a first set of weightvalues as a function of a first correlation of the first signaltransform components corresponding to a first correlation length;providing a second plurality of signal transform components for thesensor signals; calculating a second set of weight values as a functionof a second correlation of the second signal transform componentscorresponding to a second correlation length different that the firstcorrelation length; and generating an output signal as a function of thefirst weight values and the second weight values.
 31. The method ofclaim 30, wherein the first correlation length and the secondcorrelation length differ in accordance with a difference in rate ofchange of at least one frequency of at least one of the sensor signalswith respect to time.
 32. The method of any of claims 22-31, wherein thenumber of sensors is two and the hearing aid has a single, monauraloutput.
 33. The method of any of claims 22-31, wherein said calculatingis performed to minimize output variance.
 34. The method of any ofclaims 22-31, further comprising localizing a selected acoustic sourcerelative to a reference as a function of the transform components. 35.The method of any of claims 22-31, wherein the transform components areof a fourier type.
 36. A hearing aid system operable to perform themethod of any of claims 22-31.
 37. A method comprising: detectingacoustic excitation with a number of acoustic sensors, the acousticsensors providing a corresponding number of sensor signals; establishinga set of signal transform components for each of the sensor signals;tracking location of a source of the acoustic excitation relative to areference as a function of the transform components; and providing anoutput signal as a function of the location and a correlation of thetransform components.
 38. The method of claim 37, wherein the number ofsensors is two and said tracking includes determining a phase differencebetween the sensor signals.
 39. The method of claim 37, wherein thereference is a designated axis and the location is provided in the formof an azimuthal direction.
 40. The method of claim 37, wherein saidtracking includes generating an array with a number of elements eachcorresponding to a different azimuth and detecting one or more peakvalues among the elements of the array.
 41. The method of claim 37,further comprising adjusting a beamwidth factor relative to frequency.42. The method of claim 37, further comprising calculating a number ofdifferent correlation matrices and adaptively changing correlationlength of one or more of the matrices relative to at least one other ofthe matrices.
 43. The method of claim 37, further comprising steering adirection-indicating vector corresponding to the location.
 44. Themethod of claim 37, wherein said providing include generating the outputsignal by weighting the transform components to reduce variance of theoutput signal and provide a predefined gain.
 45. A device operable toperform the method of any of claims 37-44.
 46. A hearing aid systemoperable to perform the method of any of claims 37-44.
 47. An apparatus,comprising: an acoustic sensor array operable to detect acousticexcitation, said acoustic sensor array including two or more acousticsensors each operable to provide a respective one of a number of sensorsignals; and a processor operable to determine a set of frequencycomponents for each of said sensor signals and generate an output signalrepresentative of the acoustic excitation from a designated direction,said output signal being calculated from a weighted combination of saidset of frequency components for each of said sensor signals to reducevariance of said output signal subject to a gain constraint for theacoustic excitation from said designated direction.
 48. The apparatus ofclaim 47, wherein said processor is operable to calculate said weightedcombination to generally minimize said variance of said output signaland generally maintain said gain at unity.
 49. The apparatus of claim47, wherein said processor is operable to determine a number of signalweights as a function of a frequency domain correlation matrix and avector corresponding to said designated direction.
 50. An apparatus,comprising: a first acoustic sensor operable to provide a first sensorsignal; a second acoustic sensor operable to provide a second sensorsignal; a processor operable to generate an output signal representativeof acoustic excitation detected with said first acoustic sensor and saidsecond acoustic sensor from a designated direction, said processorincluding: means for transforming said first sensor signal to a firstnumber of frequency domain transform components and said second sensorsignal to a second number of frequency domain transform components,means for weighting said first transform components to provide acorresponding number of first weighted components and said secondtransform components to provide a corresponding number of secondweighted components as a function of variance of said output signal anda gain constraint for the acoustic excitation from said designateddirection, means for combining each of said first weighted componentswith a corresponding one of said second weighted components to provide afrequency domain form of said output signal; and means for providing atime domain form of said output signal from said frequency domain form.51. The apparatus of any of claims 47-50, wherein said processorincludes means for steering said designated direction.
 52. The apparatusof any of claims 47-50, further comprising at least one acoustic outputdevice responsive to said output signal.
 53. The apparatus of any ofclaims 47-50, wherein the apparatus is arranged as a hearing aid. 54.The apparatus of any of claims 47-50, wherein the apparatus is arrangedas a voice input device.
 55. The apparatus of any of claims 47-50,wherein said processor is operable to localize an acoustic excitationsource relative to a reference.
 56. The apparatus of any of claims47-50, wherein said processor is operable to track location of anacoustic excitation source relative to an azimuthal plane.
 57. Theapparatus of any of claims 47-50, wherein said processor is operable toadjust a beamwidth control parameter with frequency.
 58. The apparatusof any of claims 47-50, wherein said processor is operable to calculatea number of different correlation matrices and adaptively adjustcorrelation length of one or more of the matrices relative to at leastone other of the matrices.